CODEX Digest - 4.2.26

1) The radiologist AI workflow and the risk of medical malpractice claims.

Bernstein MH, Sheppard B, Bruno MA, et al. Nat Health. Epub 2026 Mar 10.

The delineation of accountability for AI use in decision making is emerging. This study explores how AI use affects mock jurors' perceptions of radiologist responsibility in malpractice cases. When radiologists reviewed scans only once with AI, 75% of participants assigned blame; reviewing twice (alone and with AI) lowered blame to 53%. Double-checking scans may lessen juror blame for doctors who miss findings flagged by AI.

2) A prospective clinical feasibility study of a conversational diagnostic AI in an ambulatory primary care clinic.

Brodeur P, Koshy JM, Palepu A, et al. arXiv. Epub 2026 Mar 15.

LLM-based AI systems may improve patient diagnostic conversations, but there is limited real-world evidence. This pre-print feasibility study tests Google’s Articulate Medical Intelligence Explorer (AMIE) for history taking and diagnosis presentation. The results suggest AMIE can safely collect histories and generate diagnostic suggestions, but the authors also acknowledge limited generalizability due to the study taking place at a single center and the 100 participant sample size.

3) Not all errors are created equal: assessment of amended diagnoses at a major academic center.

Fridland S, Mehta V, Jager L, et al. Am J Clin Pathol. 2026;165(3):aqag005.

Tracking and reviewing changes in pathology reports helps identify diagnostic errors. Over five years examined in this study, report changes were rare, but major mistakes usually involved mislabeling tissue as benign or cancer, which affects diagnosis. Regular monitoring and double-checking key cases can help lab teams catch serious errors.

4) Diagnostic accuracy, fairness and clinical implementation of AI for breast cancer screening: results of multicenter retrospective and prospective technical feasibility studies.

Kelly CJ, Wilson M, Warren LM, et al. Nat Cancer. Epub Mar 10.

AI can enhance breast cancer screening by improving quality and consistency while helping address radiologist shortages. In this British study across five NHS sites, AI matched or outperformed specialist radiologists. The Google-developed AI second-reader system resulted in earlier diagnosis and reduced diagnostic errors.

5) Identification of diagnostic discrepancies as a quality assurance measure in emergency medicine - a validation study.

Marcin T, Werthmüller N, Kölbener F, et al. Scand J Trauma Resusc Emerg Med. Epub 2026 Feb 11.

Diagnostic errors are a significant healthcare concern, but difficult to assess due to the need for time-intensive chart reviews. This Swiss study examines an automated tool for detecting emergency medicine diagnostic diagnostic errors by searching for diagnostic discrepancies using emergency department discharge letters, hospital discharge letters, and general practitioner notes. The tool reveals high accuracy that could help prioritize cases for further review.

6) Depression in adolescence: Screening and diagnosis in primary care in an integrated healthcare system.

Martinez MP, Chow T, Lin JC, et al. J Affect Disord. 2026;399:121017.

Early-onset depression may persist into adulthood without timely identification and treatment. This retrospective cohort study evaluates adolescent screening in primary care to support early depression diagnosis and reduce long-term mental health issues. Findings indicate that using a standard patient health questionnaire can help to identify at-risk adolescents, however only 66% of patients received at least one screening after being recommended.

7) Human in the loop artificial intelligence in healthcare: applications, outcomes, and implementation challenges.

Olawade DB, Plabon SB, Ojo A, et al. Int J Med Inform. 2026;213:106362.

Human-in-the-loop AI combines human expertise with machine intelligence to improve decision making and support ethical and safe patient care. This narrative review examines its effects on care processes such as diagnostic imaging and clinical decision support, addresses challenges like workflow integration, regulatory issues, and sustainability, and suggests future research for enhancing collaboration in healthcare.

8) False-negative screening and diagnostic mammograms in the National Mammography Database From 2010 to 2022.

Oluyemi ET, Grimm LJ, Goldman L, et al. AJR Am J Roentgenol. 2026;226(2):e2533636.

False-negative (FN) mammograms can delay breast cancer diagnoses to impact clinical outcomes. This large retrospective study examines FN rates in screening and diagnostic mammograms from the National Mammography Database, identifying links between FN rates and patient or facility characteristics. The findings highlight the need to investigate these associations further to improve quality assurance and reduce delayed breast cancer diagnoses.

9) Impact, barriers, and facilitators of blood culture diversion devices to reduce blood culture contamination and improve patient safety: a scoping review.

Otter JA, Moore LSP, Price JR, et al. J Hosp Infect. 2026;170: 197-208.

Skin bacteria contamination in blood samples often lead to extended hospital stays, unnecessary treatments, diagnostic delays, environmental waste, and higher healthcare costs. This review assesses various blood culture diversion methods, finding that both device-based and open approaches significantly reduce blood sample contamination rates.

10) Patients’ attitudes and beliefs toward artificial intelligence use in cancer care: cross-sectional survey study.

Santos Teles M, Bryl K, Chimonas S, et al. JMIR Cancer. 2026;12:e81346.

AI is increasingly used in cancer care, but patient views are not well understood. This cross-sectional study finds patients to be most comfortable with AI for cancer screening (80.2%) and least comfortable (70.4% ) with AI aiding diagnosis. About half of patients were concerned about the risks of AI implementation.

11) Diagnostic uncertainty in physicians' reasoning: a structured approach using BANI and GRACE2. 

Shimizu T. Diagnosis (Berl). 2026;13(1):20-27.

Diagnostic uncertainty remains a major challenge for individual physicians. This commentary discusses physician diagnostic doubt, explores contributing factors through case examples, and presents a six-part behavioral framework that promotes cognitive flexibility, adaptive reasoning, and empathic communication to improve diagnostic decision-making and safety.

12) QUADAS-3: a revised tool for the quality assessment of diagnostic test accuracy studies.

Whiting PF, Tomlinson E, Rutjes AWS, et al. Ann Intern Med. Epub 2026 Feb 17.

Assessing accuracy and applicability to different populations is vital for reliable diagnostic test reviews. This article presents QUADAS-3, a six-phase tool designed to assess the risk of bias and applicability of primary test accuracy studies for diagnosis, screening, or staging of disease. Notable updates include creation of an ideal test accuracy trial concept, an estimate-level assessment, a new "Analysis" domain, rationale sections for judgments, and formal overall risk and applicability evaluations.